TensorFlow Datasets load images from Path

Question

I have a dataset like this,

df = pd.read_csv('train.csv')
df.head()
>>>
   image                 label
0  /path/to/img1.jpg       1
1  /path/to/img2.jpg       0
2  /path/to/img3.jpg       0
3  /path/to/img4.jpg       1
4  /path/to/img5.jpg       1

The first column is the path to the image which is to be loaded, and the second is the label associated with that image. I want to load this with Tensorflow. I did this,

ds = tf.data.Dataset.from_tensor_slices(df.values)
>>> ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).

I know that this Error is coming because the first column is a string and the second is an integer so what's the most efficient way to load this.

So every time I get a new batch from this dataset it should give a tensor of all images and labels.

Answer 1

You need to specify the target columns. It is similar to those questions but the idea is from 6 to 33 pictures recognized.

[ Sample ]:

import tensorflow as tf
import tensorflow_io as tfio

import pandas as pd

import matplotlib.pyplot as plt

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# list_label_actual = [ 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Pikaploy', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt', 'Candidt Kibt' ]
list_label_actual = [ 'Candidt Kibt', 'Pikaploy' ]

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Dataset
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
variables = pd.read_excel('F:\\temp\\Python\\excel\\Book 7.xlsx', index_col=None, header=[0])

list_label = [ ]
list_Image = [ ]
list_file_actual = [ ]

for Index, Image, Label in variables.values:
    print( Label )
    list_label.append( Label )
    
    image = tf.io.read_file( Image )
    image = tfio.experimental.image.decode_tiff(image, index=0)
    list_file_actual.append(image)
    image = tf.image.resize(image, [32,32], method='nearest')
    list_Image.append(image)


list_label = tf.cast( list_label, dtype=tf.int32 )
list_label = tf.constant( list_label, shape=( 33, 1, 1 ) )
list_Image = tf.cast( list_Image, dtype=tf.int32 )
list_Image = tf.constant( list_Image, shape=( 33, 1, 32, 32, 4 ) )

dataset = tf.data.Dataset.from_tensor_slices(( list_Image, list_label ))
list_Image = tf.constant( list_Image, shape=( 33, 32, 32, 4) ).numpy()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential([
    tf.keras.layers.InputLayer(input_shape=( 32, 32, 4 )),
    tf.keras.layers.Normalization(mean=3., variance=2.),
    tf.keras.layers.Normalization(mean=4., variance=6.),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Reshape((256, 32 * 32)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(196, return_sequences=True, return_state=False)),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(196)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(192, activation='relu'),
    tf.keras.layers.Dense(2),
])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Callback
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
class custom_callback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if( logs['accuracy'] >= 0.97 ):
            self.model.stop_training = True
    
custom_callback = custom_callback()

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Optimizer
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
optimizer = tf.keras.optimizers.Nadam(
    learning_rate=0.000001, beta_1=0.9, beta_2=0.999, epsilon=1e-07,
    name='Nadam'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Loss Fn
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""                               
lossfn = tf.keras.losses.SparseCategoricalCrossentropy(
    from_logits=False,
    reduction=tf.keras.losses.Reduction.AUTO,
    name='sparse_categorical_crossentropy'
)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Summary
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model.compile(optimizer=optimizer, loss=lossfn, metrics=['accuracy'] )

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( dataset, batch_size=100, epochs=50, callbacks=[custom_callback] )

plt.figure(figsize=(6,6))
plt.title("Actors recognitions")
for i in range(len(list_Image)):
    img = tf.keras.preprocessing.image.array_to_img(
        list_Image[i],
        data_format=None,
        scale=True
    )
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])
    plt.subplot(6, 6, i + 1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(list_file_actual[i])
    plt.xlabel(str(round(score[tf.math.argmax(score).numpy()].numpy(), 2)) + ":" +  str(list_label_actual[tf.math.argmax(score)]))
    
plt.show()

input('...')

[ Output ]: